Egocentric Gaze Estimation.
Previous works have shown that gaze behavior plays an important role in recognizing human daily actions from egocentric videos [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}. Here, we discuss the most relevant works that develop deep models for egocentric gaze estimation. Zhang et al. [9]} used deep models and an adversarial network to predict egocentric gaze location in future video frames. Huang et al. [4]} incorporated temporal attention transition into saliency-based models for gaze estimation. Tavakoli et al. [8]} studied both top-down and bottom-up cues that contribute to gaze guidance. Park et al. [6]} introduced the novel problem of predicting joint attention during social interaction using egocentric videos. Huang et al. [7]} collected a new egocentric video dataset and developed a graphical model to detect joint attention. Thakur et al. [10]} proposed a multi-modal network that uses both video and inertial measurement unit data for more accurate egocentric gaze estimation. Naas et al. [13]} developed a tiling scheme for gaze prediction which enables a more efficient VR content delivery. Importantly, we are the first to develop a transformer-based architecture to address the problem of egocentric gaze estimation.
